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datasets: |
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- roneneldan/TinyStories |
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Model trained on the TinyStories Dataset, see https://arxiv.org/abs/2305.07759 |
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Based on GPT-Neo architecture. |
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License: mit |
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---- hyperparams used to train this model ---- |
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lr = 5e-4 |
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lr_schedule = constant |
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wd=0.1 |
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adam_beta1=0.9, adam_beta2 = 0.95 |
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context length=512 |
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batch size=80 |
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gradient accumulation steps=16 |
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------ EXAMPLE USAGE --- |
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from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig |
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model = AutoModelForCausalLM.from_pretrained('roneneldan/TinyStories-33M') |
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tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-125M") |
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prompt = "Once upon a time there was" |
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input_ids = tokenizer.encode(prompt, return_tensors="pt") |
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# Generate completion |
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output = model.generate(input_ids, max_length = 1000, num_beams=1) |
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# Decode the completion |
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output_text = tokenizer.decode(output[0], skip_special_tokens=True) |
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# Print the generated text |
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print(output_text) |
